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Record W4390659066 · doi:10.1177/02670836231217393

Microstructure effect on sliding wear of 316L stainless steel selectively laser melted

2024· article· en· W4390659066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Science and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMicrostructureNanoindentationIndentation hardnessScanning electron microscopeOptical microscopeMetallurgyDiffractionLaserComposite materialOptics

Abstract

fetched live from OpenAlex

Due to varying thermal cycles, the resulting microstructure of metal additive manufacturing differs from the conventionally processed counterpart alloys. Since the mechanical properties depend on the microstructure, the wear resistance of components manufactured by laser powder bed fusion (LPBF) is determined by the processing parameters. This work focuses on microhardness and sliding wear of 316L stainless steel, evaluated nanoindentation and pin-on-disc, respectively, analysed through optical microscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD) and glow discharge emission spectrometry (GDOES). The results show that the LPBF-processed specimens have about 40% higher microhardness and ca. 30% lower wear rate than the wrought counterpart. The enhanced sliding wear resistance is associated with the higher density of dislocations at the cellular subgrain boundaries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.210
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it